Deadwood-rich managed forests provide insights into the old-forest association of wood-inhabiting fungi

Fungal Ecology 27 (2017) 155e167

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Deadwood-rich managed forests provide insights into the old-forest association of wood-inhabiting fungi ~ hmus* Kadri Runnel, Asko Lo Department of Zoology, Institute of Ecology and Earth Sciences, University of Tartu, Vanemuise 46, 51014, Tartu, Estonia

a r t i c l e i n f o

a b s t r a c t

Article history: Received 9 May 2016 Received in revised form 6 September 2016 Accepted 28 September 2016 Available online 18 November 2016

A major question in fungal conservation is why many species are confined to old forests, and how they could be supported by contemporary landscape matrix. Specifically, forestry that retains large biological legacies across landscape could reduce old-forest dependencies to species that require unusual substrate conditions. We sampled polypores in 112 2 ha plots (both old and harvested stands) in a semi-natural forestry context in Estonia and modelled the habitat factors of species confined to old growth. The results confirmed that old-growth assemblages emerged mostly due to diverse and abundant substrate supply (notably downed CWD). Only 10 species (five spruce-dwellers) were confined to old growth; of these, only Fomitopsis rosea and Oxyporus corticola were additionally affected by forest connectivity. The forestry system studied appeared particularly favourable for the species inhabiting deciduous wood. To better address habitat degradation in conservation, expert lists of ‘old-forest (indicator) fungi’ should be replaced with evidence-based focal taxa. © 2016 Elsevier Ltd and British Mycological Society. All rights reserved.

Corresponding Editor: Jacob Heilmann-Clausen Keywords: Coarse woody debris Hemiboreal forest Indicator species Old growth Picea abies Saproxylic fungi Substrate threshold

1. Introduction In conservation literature, old intact forests have become a symbol of rich biodiversity and a habitat for many specialized species. The associations of species with old forest have been referred to using a range of closely related terms that reflect particular aspects of the association or research tradition. Long natural development of forest, for example, has been described by adjectives such as old-growth, late-successional, ancient, primeval, virgin, primary, natural, unmanaged, undisturbed, overmature, etc. Certain species have been then demonstrated or suggested to “prefer”, “require”, “depend on” or “indicate” such forests (e.g., Rose, 1976; Berg et al., 1994; Goward, 1994; Esseen et al., 1997; Carignan and Villard, 2002), or to grow, reproduce or survive better there, revealing habitat quality (e.g., Jules, 1998; Jüriado et al., 2011). Strength of the association has been called old-forest “specificity” (Hanski, 2000) or, alternatively, intolerance of human disturbance (Trass et al., 1999; Douglas et al., 2013). Because of

* Corresponding author. ~hmus). E-mail address: [email protected] (A. Lo http://dx.doi.org/10.1016/j.funeco.2016.09.006 1754-5048/© 2016 Elsevier Ltd and British Mycological Society. All rights reserved.

much inconsistency and parallel use in this terminology, and because certain studied aspects do not preclude others, we treat those concepts collectively as evidence for old-forest species. Their general feature is the importance of long-developed forest habitat, with a common implication to protect that habitat (e.g., Parmasto, 2001; Molina et al., 2006). The concept of old-forest species is based on two fundamental ecological phenomena: forest succession, and niche breadth in a particular landscape context. Forest succession affects species’ occurrences due to slow or delayed natural development of vital structures or conditions (substrates; microclimate; the biotic n assemblage), population continuity, or their combinations (Norde and Appelqvist, 2001). These patterns may be blurred by local influences of forest management (selectively removing or promoting some structures) and landscape connectivity (affecting population continuity via dispersal). The local context can also affect the range of habitats occupied (i.e., the realized niche); in the case of fungi, for example, due to interspecific interactions (Toljander et al., 2006) or other limiting factors. Therefore, most old-forest associations of species reported in the literature probably represent a mixture of causes, which are even more difficult to organize due to enormous variation in the habitats compared, methods, and reporting detail.

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A consequence is that we still do not know how to combine various set-asides with environmental requirements for forestry in order to sustain old-forest biota (e.g., Ranius and Kindvall, 2006). Among non-lichenized fungi, old-forest associations are probably best known in polypores e a polyphyletic group of (mostly) wood-decaying fungi (Parmasto, 2001; Junninen and Komonen, 2011). An obvious cause for those associations is the substrate e the rich dead wood supply of natural forests compared with timber production areas (e.g., Siitonen, 2001). Many studies have documented that the number of all (and specifically rare) polypore species in a forest stand increases along with dead wood abundance, notably with the availability of large downed trunks of various decay stages (reviewed by Junninen and Komonen, 2011). The contributions of other old-forest characteristics are less clear and under debate. For example, slow accumulation of dead wood might explain most of what has been attributed to temporal forest continuity (Ohlson et al., 1997); benefits of small-scale spatial connectivity of wood items may be restricted to an early phase of fungal colonization (Rolstad et al., 2004); and at least some putative ‘old-forest polypores’ seem to thrive on dead wood-rich disturbed €ssler et al., 2012; Runnel et al., 2014). Furthermore, most sites (Ba evidence of landscape impacts on substrate- or stand-scale occurrence of polypores comes from intensively managed North- and West-European regions (Sverdrup-Thygeson and Lindenmayer, € et al., 2006; Stokland and Larsson, 2011; Norde n 2003; Penttila et al., 2013; Abrego et al., 2015). The production-forest matrix in such landscapes has experienced large reductions in dead wood pools, tree species diversity, and natural disturbance regimes € et al., 2004), which may (Esseen et al., 1997; Siitonen, 2001; Penttila have restricted adequate dead wood conditions to old-forest remnants. In contrast, studies on biological legacies in natural forest landscapes suggest that many ‘old-forest species’ can inhabit a wide range of successional stages (Hansen et al., 1991). Hence, current old-forest associations of fungi may not represent their fundamental niches, and the populations might be able to expand to managed forests if adequate substrate amounts are provided ~ hmus and Lo ~ hmus, 2011). For polypores, efforts to achieve that, (Lo through modified silviculture and habitat restoration, may be crucial in the long run, since isolated populations in small reserves are prone to genetic impoverishment (Stenlid and Gustafsson, 2001) and stochastic extinction (Berglund and Jonsson, 2008). While simplified treatment of old-forest relationships can thus backfire in conservation, we also acknowledge that conservation managers need clear guidelines for action. This dilemma (see also Lindenmayer et al., 2006) highlights another practical problem with old-forest species: managers typically need explicit species  melis lists for enforcing habitat and species protection (e.g., Bru et al., 2011). Such lists are usually compiled based on expert opinion (e.g., Blaschke et al., 2009), which can become difficult to replace with evidence-based lists (Fig. 1; see also HeilmannClausen and Vesterholt, 2008). Specifically, when the data behind species listings becomes untraceable, new contrary evidence cannot be weighed. Another process that may create problems in fungal listings is ‘pseudo-evidence’ from uncertain red-listing of threatened species. Due to the cryptic life style of fungi, their redlisting is largely based on the status of ‘appropriate’ or ‘potential’ habitat as proxies of population size and trends (Dahlberg and Mueller, 2011). These proxies may fail in the case of contextdependent old-forest associations (see above) or when the expert knowledge has detectability- or sampling-bias, which is often the ~ hmus and Lo ~hmus, 2009; Runnel et al., 2014). National case (e.g., Lo ‘indicator’ lists and red-lists may even become circular if based on the same expert judgement (species red-listed due to their perceived association with old forest, and vice versa). There is a clear necessity for high-quality, species-scale studies to validate

and update the listings of old-forest species and, potentially, to diversify their conservation management (see also Molina et al., 2011). In this paper, we address the complexity of old-forest associations in polypores by investigating a study system where old growth is rare, but production forests have a relatively large amount of dead wood. This type of system is seen in most Estonian forests, which are managed by a medium-intensity ‘semi-natural forestry’ approach, and which maintains tree species diversity through predominantly natural regeneration and supports dead wood supply through retention and low-intensity thinning prac~ hmus and Kraut, 2010; Lo ~ hmus et al., 2013). The relatively tices (Lo high habitat quality of the resulting landscape matrix has been documented for several dead wood-dependent organisms, notably ~ hmus and Lo ~ hmus, 2011), saproxylic beetles calicioid fungi (Lo ~hmus et al., 2016). (Kraut et al., 2016), and woodpeckers (Lo Although many old-forest indicator species (including polypores) have been listed for these landscapes as well (Parmasto and Parmasto, 1997; Trass et al., 1999), we hypothesize that those oldforest associations are relatively weak. Specifically, we expect that most old-forest associations are related to substrate amounts because better connectivity across the landscape could equalize substrate colonization probabilities. To investigate that, we compared polypore assemblages and species distributions among three successional stages (old growth; mature managed forests; clearcut sites) and in relation to structural site-type and landscape factors. We asked: (1) how are putative old-forest polypore species (as listed in the literature, see Supplementary Appendices 1e2) distributed among the successional stages; (2) which species remain confined to old growth; and (3) whether high substrate amounts could account for that? 2. Materials and methods 2.1. Study area and sampling design The study was carried out in the Estonian mainland (Fig. 2). Estonia is situated in the European hemiboreal vegetation zone (Ahti et al., 1968). The mean air temperature is 17
C in July and 6
C in January and the average precipitation is 600e700 mm yr1. The topography is mostly of glacial origin: flat and undulating moraine plains as well as glaciolacustrine plains with abundant clayey deposits and extensive postglacial paludification. We used 4 h surveys of polypore fruit bodies in 2 ha plots e a field method effective in terms of species lists (Runnel et al., 2015). We included two setups of such plots, representing five common ~hmus, 1984): (i) dry boreal forest types (‘site-type groups’ sensu Lo forests (mostly Vaccinium vitis-idaea-type) on higher fluvioglacial landforms and till mounds with podzols (pHKCl 3.5e5.0) where the top layer is periodically dry and ground water deeper than 2 m; (ii) meso-eutrophic forests (mostly Oxalis-type) on till mounds or rolling plains with podzols or Stagnic Luvisols (pHKCl 3.2e4.2) where ground water is usually deeper than 2 m; (iii) eutrophic boreo-nemoral forests (mostly Aegopodium-type) predominantly on undulating sandy till plains with favorably moist (in springtime anaerobic) Gleyic Cambisols or Luvisols (pHKCl 4.7e6.5) and almost no organic horizon; (iv) mobile-water swamp forests on thin seasonally flooded Eutric Histosols and Fluvisols, with a peat layer 30 cm (pHKCl 5.0e6.5) in lowlands and valleys along rivers or around bogs; (v) drained mixotrophic peatland forests, which have developed on pine wetlands after many decades since ditching. The Vaccinium-type and drained stands were dominated by Scots pine (Pinus sylvestris); the remaining sites (collectively referred to as ‘moist-wet mixed forests’) hosted conifer/deciduous mixtures with

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157

Fig. 1. Types of sources referring to old-forest associations of fungi, their main connections by information flow, and the North-European references that served as a basis for the current study (Supplementary Appendices 1e2). Ideally, such evidence should include replicated research guided by expert knowledge (network of dashed lines). In reality, expert opinions hold a central position with often absent or unknown research base (solid lines) (Andersson et al., 2005; Anonymous, 2010; ArtDatabanken, 2015; Bader et al., 1995; €, 2008; Nitare, 2000; Rassi et al., 2010). Christensen et al., 2005; Ek et al., 2002; Henriksen and Hilmo, 2015; Niemela

Fig. 2. Locations of the 92 study plots of the main study (five regions depicted as ovals) and additional 20 plots used in the habitat modelling only (‘habitat-protection study’) in Estonia. Forest distribution is shown with the grey background.

Norway spruce (Picea abies) or, in some Oxalis-type stands, with Scots pine. All the plots were situated in contiguous lowland forest landscapes (only two plots >100 m above sea level); cultivated land covered on average only 5% of the land area within 1 km. Setup 1 (‘main study’) was established to explore forest biodi~hmus and versity along with post-harvest succession (see also Lo ~ hmus, 2009, 2011). This setup included 92 plots in five regions Lo as a factorial combination of four forest types (i-iv above)  four management stages, each in six (mobile-water swamps e five) replicates. Each replicate represented a block of four plots (a full set of the management stages) that were situated as close to each other as possible (<18 km, with two exceptions). The management stages

were: old growth (most trees 100e180 y; coniferous >125 y old; stand ages up to at least 300 y); mature commercial forests (65e95 y old; both recently thinned and unthinned; most documented to be secondary stands of clear-cut origin); clear-cuts and retention cuts with solitary retention trees (median 15, range 3e100 live trees ha1). The cut sites were usually sampled 5e11 y post harvesting (range 4e19 y); most were naturally regenerating (only four Vaccinium-type plots were mechanically scarified). We pooled the clear-cuts and retention cuts for the purposes of this paper because retention trees contributed little to polypore assemblages at the time of sampling: most trees were alive (only three parasitic polypores inhabit common retention tree species in Estonia) and those

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fallen were still fresh (forming <5% of the coarse woody debris, ~ hmus et al., 2013). CWD; Lo Setup 2 (‘habitat-protection study’) supplemented the main study with 20 plots: four forest types (no. iiev above, with mesoeutrophic plots mostly pine-dominated; Fig. 2), each replicated in five plots that were distributed equally along an age gradient from 70 to 85 y to the oldest available (131e176 y). These stands had

been protected for many years (as abandoned nest sites of a threatened bird species, Ciconia nigra) and they were only used in the habitat modelling of putative old-forest fungi (Section 2.4) e for creating a continuous range of stand ages and adding unmanaged mature stands to the sample (Fig. 3). 2.2. Polypore fruit body surveys For polypore surveys, all but six study plots were visited once. In the main study, the sites were sampled in the top fruiting season (SeptembereOctober) in 2005e2007 (one region in 2005; two regions in 2006; two regions during the whole study). The four plots in each block were always surveyed at close dates in the same season to minimize temporal variation. The habitat-protection study took place in September 2014. Repeated surveys (2e4 revisits during 2007e2015) in a subset of six main study plots (three mature managed and three old-growth plots) were performed for estimating the efficiency of the one-season surveys; these data are only used here for improving species occurrence estimates for habitat modelling (Section 2.4, analysis iv). The fieldwork followed a standard rapid-survey protocol: living fruit bodies of all polypore species (also dead fruit bodies of annual species) were searched for in each 2 ha plot during a 4 h search by A.L. (main study; a part of the re-surveys) or K.R. (habitat-protection study; a part of the re-surveys). For each species in each plot, the first ten records were described in detail (tree species; diameter; decay stage; position on or above the ground). One record refers to all fruit bodies of the same species on one substrate item (standing or fallen trunk; fallen branch; rootplate of a fallen tree). Up to 150 described records per plot could be obtained within the 4 h. Since a few species usually had >10 records in every survey, we also categorized the abundance of each species on an approximately logarithmic five-point scale: one record (1), 2e5 records (2), 6e15 records (3), 16e100 records (4) or more than 100 records (5). Such a scaling approach was a compromise between the presenceabsence data emphasizing rare species, and raw abundances that emphasize dominant species and are not cost-effective to record when compiling species lists. To reduce false-negative results in habitat modelling (but not for any other analyses), we also included a few additional records of species obtained during similar time~hmus limited surveys of other taxon groups in the same plots (Lo ~hmus, 2011; Remm et al., 2013). and Lo The fruit bodies that could not be reliably identified in the field were collected and identified microscopically. By necessity (ca. 120 specimens), we also checked the identifications by rDNA ITS and LSU sequences, as compared with those available in public reference databases and authors' personal database. The taxonomy and nomenclature mostly follow MycoBank (accessed 25.08.2016). We used collective treatment for the following taxonomically complicated or unresolved taxa: Phellinus alni/Phellinus nigricans on birch; Postia alni/Postia caesia/Postia subcaesia and their undescribed siblings (treated as ‘P. cf caesia on conifers’ and ‘P. cf caesia on deciduous trees’); Postia sericeomollis/Postia romellii; Postia tephroleuca/Postia lactea. The reference materials have been deposited in the herbarium of the University of Tartu (TU). 2.3. Measuring habitat structure

Fig. 3. Stand age (mean age of overstorey trees) in relation to the amount (A) and decay-stage diversity of downed CWD (B), and the cover of old forests (at least 100 years old) within 1 km (C). The regression lines (±95% CI) refer to all 66 forests (subsamples shown with symbols for illustrative purposes), except in (A) where Vaccinium-type forests have been distinguished from other forests.

The procedure for measuring stand structure has been described ~ hmus and Kraut (2010). In each plot, four straight in detail by Lo 50 m transect lines were spaced out to represent the whole area (in structurally poor Vaccinium-type forests, we added a fifth line for better capturing of CWD). Strip transects were used for estimating the density of all live trees and standing dead trees (including broken-top snags at least 1.3 m tall) that were 10 cm in diameter

~hmus / Fungal Ecology 27 (2017) 155e167 K. Runnel, A. Lo

at breast height. A line-intersect method was used to estimate the volumes of downed dead wood: CWD (10 cm in diameter at intersections with the line) was assessed along the whole line; fine woody debris (FWD, 0.3e9.9 cm) e at six 1 m sections per 50 m. The downed CWD volumes in all forest plots, except in the Vaccinium-type, increased significantly with stand age (Fig. 3A). In the analyses of the current study, we use the plot-scale mean estimates of the following stand characteristics, which were considered to be important for polypores: (a) substrate availability measures e total volumes of downed FWD and CWD, and CWD volumes by main tree species  size (e.g. volume of spruce logs at least 30 cm in diameter); (b) substrate diversity measures e Shannon indices of species diversity and decay-stage diversity of CWD (based on volume distribution among tree species or decay stages; see also Remm et al., 2013). The latter was interpreted to indicate continuity of CWD input in time (cf. Stokland, 2001) and, in our study scheme, it was independent of stand age (Fig. 3B; see Sverdrup-Thygeson and Lindenmayer, 2003 for a similar situation). We also estimated forest connectivity parameters within 1 km around the study plots since habitat connectivity is known to affect stand-scale occurrence and abundance of several polypore species n et al., 2013; Abrego et al., 2015). We calculated the areas of (Norde all forest and, separately, of coniferous, deciduous and mixed forests from the 1:100,000 Corine Land Cover 2006 vector map (EEA, 2007). We also estimated the total area of >100-year-old forest (at the time of the fungal survey) using the National Forest Registry data (http://register.metsad.ee/avalik/) and its attached collection of aerial photographs for unregistered forests (ca. 10% of the forested area). For ca. 75% of area, we had the registry data as of 2005; for the remaining part we interpreted the age information from 2015. Old-forest areas were more extensive around older study stands but the co-variation was relatively weak (Fig. 3C). 2.4. Data processing We carried out four sets of analyses, all based on 2 ha plots as observation units. Three sets of analyses were based on the main (balanced) setup and compared old growth, mature managed forests, and harvested plots in terms of polypore (i) assemblage composition, (ii) species richness, and (iii) management-stage preferences of individual species. Based on the latter, we (iv) added the data from the habitat-protection study and the repeated surveys of six main study plots to model the occurrences of those species that showed significant preference for old growth. We used the PC-ORD 6.07 package (McCune and Mefford, 2011) for the multivariate analyses (i) and (iii). The remaining analyses were performed with the STATISTICA 8 software (StatSoft; Tulsa, Okla, USA). (i) We analysed assemblage composition based on the species that occurred in at least three plots, and their abundance class (0e5; see above) as input data. Assemblage differences between forest types and management stages were tested using MultiResponse Permutation Procedures (MRPP) with Sørensen (BrayCurtis) distance. As a subset, we compared old growth with mature managed forests within and between the three nutrient-rich forest types (Section 3.1: ii-iv) for the species with >50% occurrences in these forests. We illustrated assemblage differences using Sørensen distances for non-metric multidimensional scaling (NMS), and estimated linear correlations between the habitat factors and ordination scores separately for the plots and species. We used the medium autopilot mode to choose the number of axes based on stress values; thereafter, the number of axes was fixed, and 250 runs with real data were performed. (ii) We used a set of general linear models (GLM; Type III sums of squares; intercept included) to assess the relative contribution of

159

substrate factors to plot-scale species richness. The residuals met the normal distribution assumptions sufficiently in all cases (Kolmogorov-Smirnov and Lilliefors tests: P > 0.05). We pooled nutrient-rich forest types (ii-iv) to increase robustness, since the assemblage analyses mostly distinguished Vaccinium sites from the others. Spatial and sampling heterogeneity were included as a random factor ‘Sample identity’ (6 levels: the five regions of setup 1 þ the setup 2; Fig. 2), which also captured differences in sampling years (see Section 2.2). We started from simple GLMs where the observed species richness across all plots was related to forest type and general substrate availability, i.e. volumes of downed CWD and FWD (also their square terms to test for non-linearity). We checked the independent effect of forest management stage by adding this variable to the model. We then modelled polypore species richness specifically in old growth and mature managed forests in three steps. (a) We identified the best candidate subsets of substrate and habitat factors based on Akaike's information criterion (the delta AICc < 2 rule; Burnham and Anderson, 2002). The factors considered were: forest type; total CWD and FWD volumes; volumes of large downed CWD (diameter 20 cm) and of downed CWD by main tree species (spruce, aspen, birch and pine); CWD diversity measures (Shannon indices of species diversity and decay-stage diversity). (b) We analysed the best subsets in order to distinguish the best substrate model as defined by largest adjusted linear fit (R2adj) and all fixed factors statistically significant (P < 0.05). In this step, we included the sample identity (random factor) and tested for the significance of square terms (non-linear relationships) of the substrate factors. (c) We tested for any remaining ‘old-growth’ effect by adding the management stage and its interaction with forest type to the best substrate model. (iii) We distinguished old-forest species through two complementary approaches. Using the main setup, we performed two sets ^ne and Legendre, 1997) based of Indicator Species Analyses (Dufre on the absolute numbers of described records (note that these analyses are conservative in the case of dominant species; see above). We first tested for differences between forests vs. harvested plots and, for the forest species detected, we separately assessed their preference for old growth over mature managed stands. Secondly, we used the habitat-protection dataset to explore correlations (Spearman's or Pearson's, depending on species abundance distributions) between stand age and species abundance. Both analyses were restricted to the species found from at least three plots and to the forest types that had at least one record. The results were compared with the listings previously published for North Europe (Supplementary Appendix 1). (iv) Similarly to the three steps of the analysis (ii), we constructed habitat models (GLMs) for each old-forest species identified in the analysis (iii). We improved the abundance estimates in the main dataset by averaging the values from all repeated surveys and adding a total of 35 casual observations (coded as 0.1 records each; see Section 2.2). We omitted the forest types where the species had <10% occurrences (to reduce noise from marginal conditions). These procedures transformed the original count data scale to a relatively continuous abundance scale. When screening for the best candidate subsets of habitat factors (Step a), we added species-specific variables based on knowledge of life strategies and host trees from the following list: area of deciduousþmixed or coniferousþmixed forests in 1 km radius; volume of large downed CWD (diameter 20 cm) of main tree species (data too scarce for aspen); the per-hectare density and share of live host trees 30 cm (in parasitic polypores). Testing of the additional effect of old-forest conditions (Step c), included two continuous variables (stand age and landscape-scale proportion of 100 year-old forests within

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1 km) and their interaction. Non-significant interactions were removed from the final model. In four species (Junghuhnia collabens, Phellinus nigrolimitatus, Porodaedalea pini, Polyporus badius) the number of occupied sites was <20, which limited the number of factors to be combined. For these infrequent species, we compared the goodness of fit (R2adj) and factor significance of two alternative models: one comprising only substrate factors and the other comprising only old-forest factors. 3. Results 3.1. Assemblage patterns We obtained a total of 7461 described records of 139 polypore species in the 92 plots of the main study (Supplementary Appendix 1). The 23 old-growth plots hosted 115 species; mature managed (n ¼ 23) and harvested plots (n ¼ 46) hosted 106 and 109 species, respectively. The total numbers in individual forest types ranged from 82 (Vaccinium) to 111 species (eutrophic sites). Twenty polypore species were found in a single plot and six species in two plots only. The most abundant species were Fomitopsis pinicola (692 described records in 88 plots), Trichaptum abietinum (618/88) and Fomes fomentarius (522/77); however, these record numbers are ca. 2e3-fold underestimates compared with rarer species, since we described only the ten first records of each species per plot. Polypore assemblages in old growth and mature stands differed significantly from those in harvested plots within all four forest types studied (Fig. 4A; MRPP: A ¼ 0.09e0.13, P < 0.001). Vacciniumtype sites were generally distinct from every other forest type (A ¼ 0.11e0.24, P  0.001); as reflected by the primary ordination axis in NMS that was near-parallel to tree species diversity of CWD (Fig. 4A). Forest types differed also within every management stage, except meso-eutrophic vs. eutrophic old-growth forests (P ¼ 0.772; Fig. 4B). Secondarily, the assemblages varied along with the dead wood pool: large FWD amounts separated harvested plots from forests (Fig. 4A), and CWD amount was a major, partly forest-age related, factor in nutrient-rich forests (Fig. 4B, cf. Fig. 3A). The assemblages in old growth tended to be more distinct from mature

stands in eutrophic (A ¼ 0.05; P ¼ 0.002) and Vaccinium-type sites (A ¼ 0.06, P ¼ 0.002), than in meso-eutrophic (A ¼ 0.03, P ¼ 0.021) and swamp sites (A ¼ 0.03, P ¼ 0.078). However, the latter formed the most specific (stable) assemblages (the smallest polygon on Fig. 4). Stand-scale species richness was greatest in nutrient-rich oldgrowth sites and varied most in nutrient-rich harvested plots (Fig. 5A). A simple combination of downed dead wood amount (notably a quadratic relationship with CWD) and forest type explained 61% of variation in species richness; the management stage and its interaction with forest type added further 5% (Table 1: part a). However, the species richness was very variable at CWD levels <50 m3 ha1 (Fig. 5B). The near-significant interaction term distinguished a management-stage impact in nutrient-rich sites (Tukey post-hoc test: P < 0.001 for all comparisons), which was lacking in the Vaccinium-type (P > 0.86). The fact that larger amounts of FWD were related to reduced species richness in this model was a co-effect due to harvested plots having much FWD (42.7 ± 18.9 SD m3/ha, cf. 19.1 ± 10.3 m3/ha in forests) but fewer polypore species. Indeed, the FWD effect disappeared when all habitat factors were explored for the most informative model in old growth and mature stands only. The latter model explained 72% of species richness by combining forest type, amount of downed CWD of spruce (only the linear term significant), and species diversity of CWD (Table 1: part b). All other models within delta AICc <2 included non-significant variables, and connectivity variables were not significant in any combination. Importantly, while the oldgrowth status of the forest improved the top model by further 10% (Table 1: part b), a similar R2adj (82%) was found for a model that only included forest type and management stage (i.e., Fig. 5A without harvested plots). 3.2. Evidence for old-forest species Of 84 Estonian polypores that have been listed as old-forest species in the literature, the main study found 48 species, of which 39 species were abundant enough for further analyses (Supplementary Appendices 1 and 2). On assemblage ordination schemes, occurrence of most of these species was centered in the

Fig. 4. Non-metric multidimensional scaling (NMS) ordination diagram of polypore assemblages in all 92 forests and harvested sites (A) and, separately, in 34 old-growth and mature managed forests on nutrient-rich sites. Each symbol refers to one 2 ha plot. Two best describing axes (% variance explained indicated) of three-dimensional solutions are shown, together with the main dead wood-factors (combined r2  0.2; lines proportional in length to the r2). Note the two outliers, which are not included in the polygons: a mesoeutrophic forest with possible historical grazing impact; a secondarily watered-up swamp clearcut. See Fig. 6 for species centroids.

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161

Fig. 5. Polypore species-richness in 2 ha plots (n ¼ 92; four-hour surveys) by management stage and Vaccinium-vs. other forest types: summary statistics (A) and relationship with the amount of downed CWD (B). (A) shows arithmetic means (points), standard deviations (boxes), the range (whiskers) and sample sizes (numbers). (B) fits a logarithmic function (R2 ¼ 0.48; ±95% CI); see Table 2 for multifactor models.

Table 1 General linear models explaining 2 ha scale species-richness of polypores in 92 plots (a), and in the subset of 46 old-growth and mature-forest plots (b) with dead wood and site factors. Model

Factora

(a) General substrate model Site type CWD m3/ha (CWD m3/ha)2 FWD m3/ha management stage added Management Site type  Management (b) Best model for forests Site type Spruce CWD (m3/ha) CWD species diversity management stage added Management Site type  Management

df

Effectsb

F

1 1 1 1

NR 0.15 ± 0.03

32.9 22.0 8.1 4.8

0.07 ± 0.03

2 2

OG

5.6 2.5

1 1 1

NR 0.08 ± 0.02 5.32 ± 2.04

20.3 22.3 6.8

1 1

OG

14.0 2.1

P

R2adj

<0.001 <0.001 <0.001 0.006 0.031 <0.001 0.006 0.086 <0.001 <0.001 <0.001 0.013 <0.001 <0.001 0.160

0.61

detected significant positive correlation with stand age in Pycnoporellus fulgens, which had an indicative old-growth preference (P ¼ 0.088) in the Indicator Species Analysis. Among these ten confirmed old-forest species, only P. pini was associated with pinedominated (Vaccinium-type) forests.

3.3. Habitat models for old-forest species 0.66

0.72

0.82

a CWD e downed coarse woody debris; FWD e fine woody debris. ‘Management’ in (b) refers to old growth compared to mature managed stands; (a) also includes harvested sites. Additionally, all models included the intercept (P < 0.001), and sample identity (random factor). b coefficients of statistically significant effects or more species-rich categories (NR, nutrient-rich; OG, old-growth).

region of nutrient-rich forests, particularly eutrophic forests and CWD-richest meso-eutrophic forests (Fig. 6A). In nutrient-rich forests, in turn, the three most demanding species in terms of CWD volume were all spruce-inhabiting (J. collabens, Flaviporus citrinellus, Fomitopsis rosea); most species positioned around low CWD values occurred on deciduous wood (vertical axis on Fig. 6B). Nine putative old-forest species were well represented both in forest and harvested plots (centroids inbetween the managementstage polygons in the ordination space), including six typical inhabitants of well-decayed wood (mostly deciduous) in moist conditions (three Ceriporia species, Rigidoporus sanguinolentus and Rigidoporus vitreus, and Junghuhnia nitida; Fig. 6A). Most putative old-forest species associated with pine-dominated (Vacciniumtype) forests did not segregate between old growth and mature stands (Postia guttulata, Postia leucomallella) or even occurred regularly on harvested plots (Junghuhnia luteoalba, P. sericeomollis) (Supplementary Appendix 1; Fig. 6A). According to the Indicator Species Analysis, statistically significant preference for old growth was detected in nine of the 39 putative old-forest species analysed (Appendix 1). Additionally, we

Habitat modelling of the ten confirmed old-forest species (six frequent and four infrequent species) showed an independent role of forest age in one frequent species only (Table 2); in two infrequent species such effect was inferred from comparing alternative models (Table 3). Dead wood continuity played no role for any species. The aspen-inhabiting Oxyporus corticola (frequent species) increasingly inhabited stands that were older, richer in downed aspen trunks, and surrounded by larger areas of old forest; the interaction between stand age and old-forest availability on the landscape indicated their compensatory effects (Table 2). Among infrequent species, old-forest models for the spruceinhabiting P. nigrolimitatus and the parasite of pine, P. pini, were clearly more informative (R2adj) than their substrate models; however, the P. pini model was only marginally significant (Table 3). The models of two frequent spruce-inhabiting species (F. rosea, P. fulgens) had sufficiently reasonable fit (R2adj ¼ 0.35e0.43) to conclude that their old-forest association mostly resulted from substrate effects. In F. rosea, that effect was fundamentally influenced by diameter of downed spruce: the 20 cm fraction provided a major positive effect but stands with increased amounts of the 10e20 cm fraction were relatively less inhabited. These two fractions were highly correlated (r54 ¼ 0.60, P < 0.001). F. rosea was the only other species (in addition to O. corticola) benefitting from landscape connectivity, but it responded to total cover of conifer and mixed forest, not specifically old forest (Table 2). The models for the remaining five species had poor fit, but all confirmed substrate relationships (three highlighting the 20 cm fraction of downed dead wood) and not additional old-forest influence. Two of these species (Phellinus laevigatus, P. badius) inhabit deciduous wood and one inhabits both deciduous and coniferous trees (Cinereomyces vulgaris). Spruce-dwellers included the only parasitic species, Phellinus chrysoloma, and the rarest species modelled e J. collabens. The old-forest model for the latter clearly highlighted stand-scale effect over the landscape-scale effect (cf. Pvalues in Table 3).

162

~hmus / Fungal Ecology 27 (2017) 155e167 K. Runnel, A. Lo

Fig. 6. Centroids of putative old-forest species in the NMS-ordination space of polypore assemblages (the scales and polygons as on Fig. 4). The species with significant indicator value for old growth in this study are in bold. See Supplementary Appendix 1 for details.

4. Discussion 4.1. Weaker old-forest associations in a favourable management context In line with our expectation, relatively few (ten out of 139) polypore species showed significant old-forest associations on current Estonian landscapes, and those associations were mostly caused by high substrate amounts. Before discussing the implications of these observations, we acknowledge two caveats to avoid misinterpretation about old-forest habitats and threatened species. First, the estimated proportion of old-forest species among polypores is probably too conservative: one-third of Estonian species were not included and many sampled species were too rare for proper testing. Thus, our analysis is primarily a critical view on ‘oldforest indicators’, which should be frequent enough for practical use (e.g., Caro, 2010) and appear in a survey of >100 sites. Regarding

such indicators, we are aware of only a few likely additions to our list; the clearest being Antrodia piceata with all (ca. 20) Estonian records from old growth (Runnel et al., 2014; eBiodiversity, 2016, and unpublished data). Second, our sampling was not designed to assess the conservation value or management of old growth remnants. In fact, the very rich fungal biota documented in Estonian old-growth reserves (e.g., Parmasto et al., 2004; Saar et al., 2007) suggests that, nowadays, any such remnant is likely to support some very rare species and deserves protection. Centuries-long impoverishment of the natural landscape is without a doubt the main cause why some polypores became regionally extinct in Estonia in the 20th century (Diplomitoporus crustulinus, Inonotopsis subiculosa, Phellinus viticola), and others are restricted to single oldgrowth locations (e.g., Amylocystis lapponica and Pycnoporellus alboluteus) (Parmasto, 2004; eBiodiversity, 2016). The situation of the latter seems to be at least stable in recent decades, notably due to national efforts to establish a representative network of forest ~ hmus et al., 2004); however, their recovery perspecreserves (Lo tives require special investigation. Keeping in mind that we did not sample the rarest species, our study nevertheless demonstrates that managed forests can support most old-forest associated polypores not only in theory, but within an economically viable forestry system that also supports several ~ hmus and Lo ~ hmus, 2011; Kraut other sensitive taxon groups (Lo ~ hmus et al., 2016; Remm and Lo ~ hmus, 2016). We et al., 2016; Lo have currently no population data to analyse how strong or reliable that supporting function is, but it apparently includes most of the ten distinguished ‘old-forest species’ as well. Of these, the oldforest association of P. laevigatus is probably even a sample error, n since this species regularly inhabits production forests (Norde et al., 2013; authors' observations; cf. our failure to extract its habitat model) and its only other old-forest listing has been made in Denmark, where forest cover is highly fragmented (Wind and Pihl, 2010). Sidera vulgaris is, by our own observations, also more widely distributed and its former listings may be partly due to € and Dai, unresolved taxonomy and misidentifications (Niemela 1997; Miettinen and Larsson, 2011). Several species apparently depend on the progress of retention forestry (Lindenmayer et al., 2012). For instance, the parasite P. pini can inhabit old pine trees without particular requirements for old growth structure; its management is primarily related to a desired balance between ~hmus, 2016). conservation and forest pathology considerations (Lo O. corticola frequently colonizes fallen retention trees in harvested areas of aspen-rich landscapes (Junninen et al., 2007; Runnel et al., 2013); note that our sampling of harvested sites took place before that phase. Even F. rosea can thrive on old retention cuts if those are burned (Suominen et al., 2015) e some modification of this practice could be re-considered in commercial forestry. Our short list of old-forest polypores had one distinct feature: it contained relatively many spruce-inhabiting species (note that all the nationally rare or extinct species listed above are also spruceinhabiting). Such species probably contributed to the pronounced species richness contrast between management stages in spruce mixedwood (Fig. 5A). Similar results in Fennoscandia have been attributed to old-forest fragmentation sensitivity of spruce€ et al., 2006; Berglund and Jonsson, 2008; dwellers (Penttila Stokland and Larsson, 2011), but such fragmentation effects were not found in our study. We, therefore, hypothesize that old-forest associated polypores on spruce represent diverse ecological strategies. The group usually documented as vulnerable in the Nordic countries includes mid- or late-decayers which are strong competitors but moderate dispersers (e.g., Holmer and Stenlid, 1997). In Estonia, these species appear old-forest associated only when they also require specific substrate, notably large trunks (F. rosea; J. collabens); the association may disappear in the case of less

~hmus / Fungal Ecology 27 (2017) 155e167 K. Runnel, A. Lo

163

Table 2 General linear models explaining plot-scale abundance of six frequent species that had significant old-forest (OG) preference. Species (no. of records/plots)a

Model and factorb

Cinereomyces vulgaris (25/22)

(a) Best substrate model Birch CWD d  20 (m3/ha) (b) … OG variables added OG proportion within 1 km Stand age (yrs) (a) Best substrate model Spruce CWD d  20 (m3/ha) Spruce CWD d < 20 (m3/ha) Conifer/mixed forest within 1 km (ha) (b) … OG variables added OG proportion within 1 km Stand age (yrs) (a) Best substrate model Aspen CWD (m3/ha) (b) … OG variables added OG proportion within 1 km Stand age (yrs) OG proportion  Stand age (a) Best substrate model Prop. live spruce d  30 (b) … OG variables added OG proportion within 1 km Stand age (yrs) (a) Best substrate model CWD d  20 (m3/ha) (b) … OG variables added OG proportion within 1 km Stand age (yrs) (a) Best substrate model Spruce CWD (m3/ha) (b) … OG variables added OG proportion within 1 km Stand age (yrs)

Fomitopsis rosea (46/22)

Oxyporus corticola (44/26)

Phellinus chrysoloma (41/30)

Phellinus laevigatus (52/29)

Pycnoporellus fulgens (52/33)

Coefficient

F

0.01 ± 0.01

7.7 0.8 1.7

0.03 ± 0.01 0.06 ± 0.03 1.58 ± 0.66

31.1 4.6 5.8 0.1 <0.1

0.03 ± 0.01

18.0

11.64 ± 3.91 0.03 ± 0.01 0.12 ± 0.03

8.9 15.8 13.7

3.0 ± 0.2

5.7 1.8 1.1

0.01 ± 0.01

5.4 0.2 2.0

0.01 ± 0.01

18.3 0.2 <0.1

P

R2adj

0.023 0.008 0.039 0.375 0.197 <0.001 <0.001 0.038 0.021 0.001 0.746 0.944 <0.001 <0.001 <0.001 0.005 <0.001 0.001 0.245 0.020 0.272 0.189 0.290 0.292 0.024 0.280 0.648 0.162 <0.001 <0.001 0.001 0.682 0.844

0.16 0.16

0.43

0.40

0.33 0.49

0.04 0.04

0.02 0.04

0.35 0.33

a

Phellinus laevigatus was analysed across all forest plots (n ¼ 66); the remaining species in all plots except the Vaccinium-type (n ¼ 54). df ¼ 1 for all effects. All models additionally included an intercept and sample identity (defined by study years and regions) as a random factor. Model coefficients are shown for significant effects only. b

Table 3 Alternative general linear models (substrate or old-forest factors) explaining plot-scale abundance of four infrequent species that had significant old-forest (OG) preference. Species; no. of records/plots

Model and factora

Junghuhnia collabens (14/11)

(a) Best substrate model Spruce CWD d  20 (m3/ha) (b) Old-forest model OG proportion within 1 km Stand age (yrs) (a) Best substrate model Spruce CWD (m3/ha) (b) Old-forest model OG proportion within 1 km Stand age (yrs) (a) Best substrate model Prop. live pines d  30 (b) Old-forest model OG proportion within 1 km Stand age (yrs) (a) Best substrate model Aspen CWD (m3/ha) (b) Old-forest model OG proportion within 1 km Stand age (yrs)

Phellinus nigrolimitatus (26/18)

Porodaedalea pini (119/14)

Polyporus badius (24/16)

Coefficient

F

0.01 ± 0.001

7.4 0.1 4.3

0.01 ± 0.003

11.2 2.0 8.3

53.8 ± 20.3

7.0 <0.1 8.5

0.02 ± 0.01

6.6 0.9 2.6

P

R2adj

0.105 0.009 0.327 0.771 0.045 0.023 0.002 0.007 0.167 0.006 0.103 0.024 0.080 0.954 0.002 0.177 0.013 0.633 0.345 0.117

0.09 0.03

0.16 0.23

0.35 0.44

0.061 ¡0.03

a Porodaedalea pini was analysed in the Vaccinium-type and drained forests only (n ¼ 17); the remaining species in all plots except the Vaccinium-type (n ¼ 54). df ¼ 1 for all effects.

substrate-specific fungi such as Phellinus ferrugineofuscus (cf. € et al., 2006; Norde n et al., 2013). The local stand-age impact Penttila observed in P. nigrolimitatus may rather be related to its delayed production of fruit bodies (Ovaskainen et al., 2013) than colonization delay, which should show a relationship with old-forest distribution (Sverdrup-Thygeson and Lindenmayer, 2003; but see also Stokland and Kauserud, 2004). Secondly, there appears to be a

group of species requiring high substrate amounts, which most typically develop after disturbance in fluctuating, possibly semi€ssler and open conditions. F. citrinellus is a prime example (Ba Müller, 2010; B€ assler et al., 2012). Our habitat model for P. fulgens suggests similar behaviour, which is supported by its documented competitive advantage in fluctuating environments (Toljander et al., 2006). The reason why these species appear together with

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~hmus / Fungal Ecology 27 (2017) 155e167 K. Runnel, A. Lo

the first group as continuity- and naturalness-dependent in Fenn et al., 2013) might be that their primary habitats noscandia (Norde have been eliminated through salvage logging and fire suppression there, and have not been well sampled. In Estonia, their distribution probably follows that of spruce-CWD hotspots, including oldgrowth remnants, large windthrows, and occasional clearcuts (this study; see also Runnel et al., 2014; for Antrodia cretacea). 4.2. Major habitat factors for polypores in Estonia Substrate supply was the main general factor that shaped polypore assemblages in mainland Estonia. The specific qualities highlighted were: (i) large-diameter downed dead wood and (ii) tree species diversity; and (iii) distinct responses of the assemblages inhabiting pine, spruce, and deciduous wood. We acknowledge that this list of key issues is not exhaustive. In particular, our treatment does not cover the habitats of mycorrhizal polypores (genera Albatrellus, Boletopsis and Coltricia) that were poorly represented in our data. (i) It seems to universally hold that polypore diversity cannot be sustained at even high total amounts of dead wood, unless it contains large trunks that are required by certain species (Junninen and Komonen, 2011; Hofmeister et al., 2015). In Estonia, the supply of large fallen trees was a major cause of contemporary old-forest associations. However, fruit body surveys may provide a biased view of tree-size requirements because some species may inhabit r et al., fine fractions without producing sporocarps (e.g., Allme 2009). The latter is the most plausible explanation for two unexpected habitat effects found in F. rosea e it benefitted from general forest cover (not specifically old forest), while being distinctly absent from typical managed stands where smaller fractions of downed spruce dominated (Table 2). Perhaps, F. rosea inhabits the production forest matrix mostly as mycelium and these areas can connect populations either through mycelial dispersal by saproxylic beetles (Johansson et al., 2006) or occasional fruit body for~hmus, 2011a). Molecular studies of mation in mature stands (Lo wood samples in production forests are needed to explore such possible hidden reservoirs of putative old-forest fungi. In fact, the pine-inhabiting J. luteoalba has been recently removed from the Swedish Red List based on molecular records from stumps (Kubart et al., 2016). Our observations confirm its frequent formation of fruit bodies in the Estonian production forests (Runnel et al., 2015; this study). (ii) In general, species habitat modelling is a superior tool for n et al., 2013), understanding polypore assemblage patterns (Norde but substrate diversity effects were best revealed in assemblage analyses. We found that the influence of tree species diversity was pronounced, while that of decay-stage diversity (also a proxy for dead wood continuity) was not. Specifically, linear effects of treespecies diversity and downed CWD of spruce outperformed a threshold model of CWD amount (Fig. 5B) in forests. This suggests that the species richness threshold was mostly caused by accumulating substrate diversity (distinct fungal assemblages) and, possibly, by the requirements of the spruce-inhabiting guild for elevated substrate amounts (see above; cf. Juutilainen et al., 2014). Note that threshold relationships for Fennoscandian polypore richness have been attributed to aggregate species-level responses € et al., 2006), which may not to habitat fragmentation (e.g., Penttila be the main mechanism in Estonian better connected forest landscapes. Our observation provokes a testable hypothesis that such thresholds are lower in more diverse forests, such as those regen~ hmus and Lo ~ hmus, 2011). It also superated naturally (see also Lo ports a practical forestry guideline to retain dead wood of all tree species in equal amounts unless more informed decisions can be made for threatened taxa (Runnel et al., 2013).

(iii) The wood diversity effects appeared to converge into a broad grouping of three types of polypores that also differ in their old-forest association: the species inhabiting spruce, deciduous wood, and pine forests. Similar distinctions have been drawn in € et al., 2006; Stokland and Fennoscandia (Penttil€ a et al., 2006; Simila Larsson, 2011). We discussed the spruce-dwellers in the first section of the Discussion, and pine-forest fungi seem to require additional work since our samples for these sites were the smallest ~hmus and Kraut, 2010). A and possibly least representative (Lo major finding to be highlighted here is that the deciduous-dwellers showed generally low dead wood requirements and only one apparent old-forest species (O. corticola on aspen). These results ~ hmus, have independent support from other Estonian studies (Lo 2011a; Runnel et al., 2013), with regular occurrence in managed forests documented for several species that are threatened by forest management elsewhere (e.g., Ceriporiopsis aneirina, Phellinus populicola, Protomerulius caryae and Rigidoporus crocatus). Furthermore, at least two Fennoscandian studies conclude that deciduousdwellers have generally low specificity regarding forest structure or n et al., 2013). connectivity (Markkanen and Halme, 2012; Norde Hence, the main reason for their old-forest association there is the intensive conifer-based silviculture, which can be mitigated by promoting mixed or deciduous forests through natural regenera~ hmus, 2011a). There are, however, specific cases, not tion (see Lo supported by natural regeneration alone. Thus, late-successional deciduous trees were also confined to old growth in our study ~hmus and Kraut, 2010), but their influence on polypore plots (Lo associations appeared insignificant. Probably more important is the decline of oak (Quercus robur) throughout hemiboreal Europe due to failing regeneration (e.g., Ikauniece et al., 2012). In Estonia, oak hosts several threatened polypores (Grifola frondosa, Hapalopilus croceus, Inocutis dryophila (Inonotus dryophilus), Pachykytospora tuberculosa), which nowadays mostly occur outside forests e in parks, cemeteries and wooded meadows. Inonotus dryadeus even went extinct in the 19th century (Parmasto, 2004). Such secondary habitats are presumably much impoverished since dead wood amount appears to be the main factor of fungal diversity also in oak stands (Irsenaite and Kutorga, 2007). 4.3. Implications: from indicator to focal species lists Our study indicates that rigid lists of ‘old-forest species’ (and similar terms, see Introduction) can be misleading in forest biodiversity conservation, which combines nature reserves (‘land sparing’) and environmental restrictions on forest exploitation (‘land sharing’). The first reason is that species differ in their limiting factors and can be redistributed depending on how the landscape is managed. Fungi on deciduous trees constitute a clear case: they can become old-growth associated in conifer-based € et al., 2004), but thrive in natintensive forestry systems (Penttila ural regeneration-based seminatural forestry (our study). The second source of obscurity is that old-forest associations may or may not refer to a conservation problem. For example, tiny remnant populations confined to old-forest fragments may face extinction unless critical habitat conditions are restored on the surrounding managed landscape (Hanski, 2000). In such cases, the label ‘oldforest species’ almost carries a flag of failure, which should be removed for the sake of the species. Conversely, a similar association in a region with much old forest left may neither imply poor status of the species or the landscape, nor a necessity for restoration. Both these situations are better captured in the process and terminology of red-listing (and de-listing) forest species. Finally, old forest may even not be the optimal natural habitat for a species, but it may be the preferred conservation option for the human society. Some species favouring naturally regenerating disturbance

~hmus / Fungal Ecology 27 (2017) 155e167 K. Runnel, A. Lo

€ssler and Müller, 2010). In brief, areas serve as an example (e.g., Ba the term ‘old-forest species’ can broadly depict species' distribution among habitats in an area and a snapshot of time, but it has no direct link with conservation goals or actions. The interpretational heterogeneity of ‘old-forest species’ is magnified by a loose usage of indicator concepts. There is now extensive literature on both theoretical and practical problems with indicator species for habitat conservation (e.g., Lindenmayer et al., 2000; Caro, 2010; Lindenmayer and Likens, 2011; Sætersdal and Gjerde, 2011). Most of that criticism applies to fungal indicators, including their spatial and temporal variability (unreliability), poor detectability (cost-effectiveness), unclear objectives or untested relationships with these objectives (vagueness). Based on those insights and the current study, we propose that lists of ‘indicator fungi’ should be replaced with two more specific approaches in forest conservation, although the general idea of ‘old-forest fungi’ might still serve educational purposes. (i) When species-level (‘fine-filter’) arguments are used for selecting set-asides (especially large reserves), these would become better justified when based on regional or global red-lists, rather than ‘indicator species’. Critical features of the set-asides (such as size or management regime) can be also more meaningfully determined for target populations than ‘for indicators’. In a favourable policy context, most work for representative reserve networks can actually be done cost~ hmus et al., 2004; effectively without species information (Lo Bouchard and Boudreault, 2016), but red-listed species may have a role for elaborating the provisional networks (e.g., Angelstam et al., 2011). (ii) Forest conservation policy needs species-level indicators to measure hidden, slow, or large-scale processes (e.g., Angelstam et al., 2004). Such indicators would be most transparent, however, when they causally relate to specific environmental changes to be mitigated, avoided, or reversed. The fact that fungal species differ in their sensitivity to habitat changes at distinct scales n et al., 2013) leads us to the ‘focal species’ concept of (e.g., Norde Lambeck (1997, 2002): we should be searching for a set of well tested and detectable species, each of which is highly sensitive to particular habitat change, and that collectively cover known and predicted threats. It is unclear what might be the share of fungi in such sets, but our screening of Estonian forest issues shows the need to combine taxon groups (e.g., Remm et al., 2013). Noss (1999) proposed seven basic characteristics to be covered by species-level indicators in forest conservation. We suggest that old-forest fungi might be mostly considered for three of these: resource limitation; dispersal limitation; and keystone functions. We propose the following ‘start-up list’ of focal fungi for Baltic forests based on our study, species habitat models from Fennon et al., 2013), and the relative abundance and scandia (Norde detectability issues of the species in the field. (1) F. rosea e for monitoring isolation effects and local quality of forest reserves on fertile soils (spruce forests and mixedwood). It may be well suited also for representing natural forest in terms of wood quality (Edman et al., 2006), area, and disturbance regime (Berglund et al., 2011), and its fruit bodies support specialized food webs (Komonen et al., 2000). (2) P. fulgens e for monitoring adequate levels of spruce dead wood across landscapes and regions, including production forests and all successional stages of forest. This conspicuous species could be integrated to large-scale forest surveys, with a focus on temporal changes in abundance and distribution. (3) O. corticola e for developing functional landscape-scale networks for aspen-inhabiting biota, combining stable populations in overmature stands and the dynamic patchwork created by retention harvesting approaches. Such a system is much needed and cannot ~ hmus, 2011b). (4) P. pini e a be maintained by old-forest reserves (Lo keystone species for tree-cavity supply in pine forests, where it ~ hmus, 2016). Additionally, limits cavity-nesting animals (Lo

165

polypores might contribute to several other issues that were outside the scope or design of our study. In pine forests, for example, the quality of the slowly developing pool of downed dead wood might be addressed by some representatives of their rich fungal biota (e.g., Junninen et al., 2006). Among deciduousdwellers, P. caryae appears to be distinctly limited by substrate amount (Markkanen and Halme, 2012; authors' observations; note identificability problems, however) and the conspicuous Pycnoporus cinnabarinus could reveal the quality in early successional habitats (clearcuts). For example, Parmasto (2004) has mentioned a long-term decline for the latter in Estonia. To summarize, the fungi that have been so far considered oldforest associated have a potential to contribute to coherent biodiversity assessment schemes in forest landscapes, notably by specifying the general ‘dead wood’ indicator for sustainable forestry (Lassauce et al., 2011). Simple reports of dead wood amounts suffer € derberg from problems with inconsistent measurement (e.g., So et al., 2014) and lacking components of ecological interactions ~hmus et al., 2016). Developing focaland long-term influence (Lo species schemes through multi-disciplinary research and testing by practitioners thus appears as a rewarding opportunity for fungal biologists in the near future. Acknowledgments We dedicate this paper to the memory of Prof. Erast Parmasto, whose pioneering work for fungal conservation inspired a whole new generation of forest biologists, including ourselves. Many ~ hmus, Piret Lo ~ hmus, people (notably Kadi Jairus, Ann Kraut, Liisa Lo Raul Rosenvald and Kristel Turja) provided technical help for the current study with site selection, forest-structure measurements, and organizing the field data. Kaisa Junninen, Otto Miettinen and Viacheslav Spirin helped to identify some difficult polypore specimens, and Ants Kaasik assisted with non-linear regression. Rasmus Puusepp did most of the work in the molecular lab. Ann Kraut, Piret ~hmus, Kadri Po ~ldmaa, Liina Remm, Kaisa Junninen and an Lo anonymous reviewer commented on various drafts of the manuscript. Funding was provided by the Estonian Research Council (projects IUT34-7, ETF6457, ETF9051) and the Environmental Investment Centre (project SLTOM16028). Supplementary data Supplementary data related to this article can be found at http:// dx.doi.org/10.1016/j.funeco.2016.09.006. References Abrego, N., B€ assler, C., Christensen, M., Heilmann-Clausen, J., 2015. Implications of reserve size and forest connectivity for the conservation of wood-inhabiting fungi in Europe. Biol. Conserv. 191, 469e477. Ahti, T., H€ amet-Ahti, L., Jalas, J., 1968. Vegetation zones and their sections in northwestern. Eur. Ann. Bot. Fenn. 5, 169e211. r, J., Stenlid, J., Dahlberg, A., 2009. Logging-residue extraction does not reduce Allme the diversity of litter-layer saprotrophic fungi in three Swedish coniferous stands after 25 years. Can. J. For. Res. 39, 1737e1748. Andersson, L., Kriukelis, R., Skuja, S., 2005. Woodland Key Habitat Inventory in Lithuania. Lithuanian Forest Inventory and Management Institute, Vilnius. Angelstam, P., Andersson, K., Axelsson, R., Elbakidze, M., Jonsson, B.G., Roberge, J.M., 2011. Protecting forest areas for biodiversity in Sweden 1991e2010: the policy implementation process and outcomes on the ground. Silva Fenn. 45, 1111e1133. Angelstam, P., Boutin, S., Schmiegelow, F., Villard, M.A., Drapeau, P., Host, G., €nkko € nen, M., Niemela €, J., 2004. Innes, J., Isachenko, G., Kuuluvainen, T., Mo Targets for boreal forest biodiversity conservation e a rationale for macroecological research and adaptive management. Ecol. Bull. 51, 487e509. €€ €a €riselupaiga Anonymous, 2010. Va ariselupaiga klassifikaator, valiku juhend, va ~lmimine ja v€ €riselupaiga kasutuso ~ iguse arvutamise kaitseks lepingu so aa €psustatud alused. Riigi Teataja I (16.12.2010), 3 (in Estonian). ta €dlistade arter i Sverige 2015. ArtDatabanken, Swedish ArtDatabanken, 2015. Ro

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